Dynamic monitoring method and system of black soil ecological environment based on intelligent communication

Abstract

The present invention relates to the technical field of ecological dynamic intelligent monitoring, and specifically to a black soil ecological environment dynamic monitoring method and system based on intelligent communication. The method comprises the following steps: setting an analysis and processing module for black soil ecological dynamic monitoring data in an intelligent communication device, and using the module to obtain a dynamic monitoring data set of the black soil ecological environment; setting monitoring demand parameters of the black soil ecological environment according to the data set, and building a multi-level evaluation framework of the black soil ecological environment in combination with the data set and the monitoring demand parameters; introducing a fuzzy comprehensive evaluation method to screen and weight analyze the evaluation framework, and obtaining the screening results and weight distribution results of the evaluation framework; establishing a numerical transformation evaluation and feedback mechanism, obtaining the score evaluation results and grade division results of the evaluation framework according to the mechanism and the data set, and combining the score evaluation results, the grade division results and the dynamic monitoring data set to realize the dynamic analysis and real-time monitoring of the black soil ecological environment.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological dynamic intelligent monitoring, and specifically to a method and system for dynamic monitoring of the ecological environment of black soil based on intelligent communication. Background Art

[0002] Black soil is an important agricultural resource in my country. However, due to the influence of various factors such as long-term over-cultivation and climate change, black soil is facing serious degradation problems, such as soil thinning, decreased fertility, and hard texture. These problems have seriously affected the agricultural production capacity of black soil. Therefore, the establishment of a black soil ecological dynamic monitoring method and system will help to timely grasp the ecological environment of black soil, scientifically formulate protection strategies, and improve agricultural production efficiency.

[0003] Traditional black soil monitoring methods mainly rely on fixed-point monitoring and sampling surveys, which have problems such as limited environmental monitoring scope, low data accuracy and difficulty in real-time grasp. The specific manifestations are as follows: fixed-point monitoring can usually only be carried out in a limited area and cannot fully reflect the overall situation of the black soil ecological environment; although sampling surveys can obtain more detailed data, the data accuracy is often insufficient due to factors such as the number, distribution and sampling methods of sampling points; traditional methods cannot achieve real-time ecological monitoring and cannot obtain dynamic changes in the black soil ecological environment in a timely manner.

[0004] It is necessary to optimize the existing ecological environment monitoring technology, add intelligent communication equipment to monitor the various parameters of the black soil ecological environment in real time, and use intelligent modules to improve the accuracy of data collection and transmission, which will help to achieve comprehensive and accurate monitoring and analysis of the black soil ecological environment. Summary of the invention

[0005] In view of the shortcomings of existing methods and the needs of practical applications, in order to realize the real-time collection, transmission and processing of ecological environment data, a high-quality black soil ecological environment database is formed, which is conducive to timely obtaining the dynamic change information of the black soil ecological environment, and is of great significance for mastering the ecological environment status of black soil, formulating scientific protection strategies, and improving agricultural production efficiency. On the one hand, the present invention provides a black soil ecological environment dynamic monitoring method based on intelligent communication, including the following steps: setting an analysis and processing module for black soil ecological dynamic monitoring data in an intelligent communication device, and using the analysis and processing module to obtain a dynamic monitoring data set of the black soil ecological environment; setting the monitoring demand parameters of the black soil ecological environment according to the dynamic monitoring data set, and combining the dynamic monitoring data set and the monitoring demand parameters to construct a multi-level evaluation framework of the black soil ecological environment; introducing a fuzzy comprehensive evaluation method to screen and weight analyze the multi-level evaluation framework, and obtain the screening results and weight distribution results of the multi-level evaluation framework; establishing a numerical transformation evaluation and feedback mechanism, and obtaining the score evaluation results and grade division results of the multi-level evaluation framework according to the numerical transformation evaluation and feedback mechanism and the dynamic monitoring data set, and combining the score evaluation results, the grade division results and the dynamic monitoring data set to realize the dynamic analysis and real-time monitoring of the black soil ecological environment. The present invention sets up an analysis and processing module for black soil ecological dynamic monitoring data, constructs a multi-level evaluation framework, introduces a fuzzy comprehensive evaluation method and a numerical transformation evaluation and feedback mechanism, which can significantly improve the accuracy and scientificity of the black soil ecological environment monitoring method, and provide a guarantee for the protection and sustainable development of the black soil ecological environment.

[0006] Optionally, the analysis and processing module for setting the dynamic monitoring data of the ecological environment of black soil in the intelligent communication device includes: setting the analysis and processing module for the dynamic monitoring data of the ecological environment of black soil based on the monitoring situation of the intelligent communication device, and the analysis and processing module includes a communication device receiving data analysis model, a communication abnormality data detection index, and a communication data optimization model. The analysis and processing module of the present invention includes an analysis and processing module for a communication device receiving data analysis model, a communication abnormality data detection index, and a communication data optimization model, which can significantly improve the data processing efficiency, communication stability and reliability of the intelligent communication device in the dynamic monitoring of the ecological environment of black soil, as well as the intelligence level of the monitoring system, and is conducive to the effective protection of the ecological environment of black soil.

[0007] Optionally, the communication device receives a data analysis model that satisfies the following relationship: in, Represents the monitoring data set received by the intelligent communication device, Indicates the communication load of intelligent communication devices, Indicates the monitoring data sampling time of the intelligent communication device, Represents the fractional adaptive spread spectrum of intelligent communication equipment, Indicates the number of monitoring data collection points of intelligent communication devices, represents the data subsets at different monitoring data collection points, Indicates the communication width of the intelligent communication device. The model of the present invention helps to identify and process abnormal monitoring data, thereby ensuring the accuracy and reliability of the data received by the system, and providing a reference for subsequent monitoring analysis and decision-making.

[0008] Optionally, the analysis and processing module for the dynamic monitoring data of the black soil ecology is set based on the monitoring situation of the intelligent communication device, and the analysis and processing module includes a communication device receiving data analysis model, a communication abnormal data detection index, and a communication data optimization model, including: setting a communication abnormal data detection index, and the communication abnormal data detection index includes a data leakage rate of the intelligent communication device; the data leakage rate satisfies the following relationship: in, Indicates the data leakage rate of intelligent communication equipment, Represents a collection of data sent to an intelligent communication device. It represents the monitoring data set received by the intelligent communication device. The communication abnormal data detection index of the present invention is of great significance for improving data integrity, enhancing system stability, optimizing resource utilization and promoting the protection of the ecological environment of black soil.

[0009] Optionally, the communication data optimization model satisfies the following relationship: in, Represents the reference value for data iterative optimization. Indicates the number of monitoring data collection points of intelligent communication devices, Represents the data sample points in different monitoring data collection points, Represents the data set of all monitoring data collection points, The data sample cluster centers representing different monitoring data collection points are shown in Figure 1. The present invention continuously approaches the true distribution of data through an iterative optimization method, thereby reducing analysis deviations caused by data errors and helping to improve the reliability of black soil ecological dynamic monitoring data.

[0010] Optionally, the use of the analysis and processing module to obtain a dynamic monitoring data set of the ecological environment of the black soil includes: obtaining an original monitoring data set of the ecological environment of the black soil based on the communication device receiving data analysis model in the analysis and processing module; evaluating the data quality of the original monitoring data set using a communication abnormality data detection index; iteratively optimizing the original monitoring data set based on the data quality and the communication data optimization model, and obtaining a dynamic monitoring data set of the ecological environment of the black soil. In the process of data iterative optimization, the present invention can remove redundant information in the original data, which helps to reduce the storage cost of system data and improve data storage efficiency.

[0011] Optionally, the monitoring requirement parameters of the black soil ecological environment are set according to the dynamic monitoring data set, and the multi-level evaluation framework of the black soil ecological environment is constructed by combining the dynamic monitoring data set and the monitoring requirement parameters, including: introducing an ecological pressure state response model, using the ecological pressure state response model to identify the dynamic monitoring data set, and setting the monitoring requirement parameters of the black soil ecological environment based on the identification result; and combining the analytic hierarchy process, the monitoring requirement parameters and the dynamic monitoring data set to construct a multi-level evaluation framework of the black soil ecological environment. The present invention sets monitoring requirement parameters according to the dynamic monitoring data set, and constructs a multi-level evaluation framework in combination with the parameters and the data set, which is of great significance for promoting the protection and sustainable development of the black soil ecological environment.

[0012] Optionally, the introduction of the fuzzy comprehensive evaluation method to screen and weight analyze the multi-level evaluation framework includes: analyzing the first-level relative weight of the first-level indicator layer in the multi-level evaluation framework to the black soil ecology based on the judgment matrix; analyzing the second-level relative weight of the second-level indicator layer to the first-level indicator layer in the multi-level evaluation framework based on the judgment matrix; using the first evaluation threshold and the fuzzy comprehensive evaluation method to screen and construct a comprehensive score set of the second-level indicators under the first-level indicator layer; using the second evaluation threshold and the fuzzy comprehensive evaluation method to screen and construct a comprehensive score set of the third-level indicators under the second-level indicator layer. The present invention introduces the fuzzy comprehensive evaluation method to screen and weight analyze the multi-level evaluation framework, improves the evaluation accuracy, optimizes the evaluation framework, and enhances the accuracy of the evaluation results.

[0013] Optionally, the establishment of a numerical transformation evaluation and feedback mechanism, and obtaining score evaluation results and grade division results of a multi-level evaluation framework based on the numerical transformation evaluation and feedback mechanism and the dynamic monitoring data set include: establishing a numerical transformation evaluation and feedback mechanism based on a multi-level evaluation framework, wherein the numerical transformation evaluation and feedback mechanism includes a multi-level indicator numerical normalization method; and obtaining score evaluation results and grade division results of different indicator layers in the multi-level evaluation framework based on the multi-level indicator numerical normalization method and the dynamic monitoring data set. The present invention can convert indicator values ​​of different dimensions and ranges into a unified metric through a multi-level indicator numerical normalization method, which helps to eliminate dimensional differences between different indicators and improve the objectivity and comparability of evaluation results.

[0014] In the second aspect, in order to efficiently execute the black soil ecological environment dynamic monitoring method based on intelligent communication provided by the present invention, the present invention also provides a black soil ecological environment dynamic monitoring system based on intelligent communication, the above system includes a processor, an input device, an output device and a memory, the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the black soil ecological environment dynamic monitoring method based on intelligent communication as described in the first aspect of the present invention. The black soil ecological environment dynamic monitoring system based on intelligent communication of the present invention has a compact structure and stable performance, and can stably execute the black soil ecological environment dynamic monitoring method based on intelligent communication provided by the present invention, thereby improving the overall applicability and practical application ability of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a method for dynamic monitoring of black soil ecological environment based on intelligent communication of the present invention; Figure 2 It is a schematic diagram of data signal changes in the intelligent communication device before and after the black soil ecological environment dynamic monitoring method based on intelligent communication of the present invention; Figure 3 This is a structural diagram of the black soil ecological environment dynamic monitoring system based on intelligent communication of the present invention. DETAILED DESCRIPTION

[0016] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0017] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0018] See also Figure 1 In order to realize the real-time and efficient collection, transmission and processing of black soil ecological environment data, a high-quality database that comprehensively reflects the black soil ecological environment is obtained, which is helpful to realize the dynamic analysis of the black soil ecological environment, grasp the overall situation, formulate protection strategies and agricultural production plans. The present invention provides a black soil ecological environment dynamic monitoring method based on intelligent communication, which includes the following steps: S1. An analysis and processing module for dynamic monitoring data of black soil ecology is set in the intelligent communication device, and a dynamic monitoring data set of the black soil ecological environment is obtained by using the above analysis and processing module. The specific steps and implementation contents are as follows: First, an analysis and processing module for the dynamic monitoring data of the black soil ecology is set up based on the monitoring conditions of the intelligent communication equipment. In the embodiment, the above-mentioned analysis and processing module mainly includes a communication equipment receiving data analysis model, a communication abnormality data detection index and a communication data optimization model.

[0019] In this embodiment, a review of domestic and foreign literature, historical black soil ecological environment monitoring records, and the current black soil ecological status are used as a black soil ecological dynamic monitoring database. In order to achieve effective collection, acquisition, and analysis of ecological data, this embodiment adds intelligent communication technology to achieve automatic detection and real-time reception of the ecological dynamic monitoring database, thereby obtaining higher-quality black soil dynamic monitoring data. Further, an analysis and processing module for black soil ecological dynamic monitoring data is designed in the embodiment. After the above-mentioned communication device receives the ecological dynamic monitoring database of the black soil, it will first be sent to the communication device receiving data analysis model. The above-mentioned model will process the ecological dynamic monitoring database, thereby extracting the monitoring data set received by the intelligent communication device.

[0020] The above communication device receives data analysis model, which satisfies the following relationship: in, Represents the monitoring data set received by the intelligent communication device, Indicates the communication load of intelligent communication devices, Indicates the monitoring data sampling time of the intelligent communication device, Represents the fractional adaptive spread spectrum of intelligent communication equipment, Indicates the number of monitoring data collection points of intelligent communication devices, represents the data subsets at different monitoring data collection points, Indicates the communication width of the intelligent communication device.

[0021] The monitoring data set received by the intelligent communication device refers to a series of ecological environment monitoring data collected and transmitted back by the intelligent communication device from domestic and foreign literature databases, historical monitoring records and black soil ecological environment within a specific time period. The above data include but are not limited to various ecological environment parameters, such as soil moisture, soil temperature, soil pH value, vegetation coverage, air quality indicators, meteorological data, etc.

[0022] Intelligent communication equipment will receive the above data in real time or periodically, and transmit it to the central data processing system or cloud server through wireless communication technologies such as Wi-Fi, Bluetooth, LoRa, NB-IoT, etc., which will help to achieve comprehensive, accurate and timely understanding and monitoring and analysis of the black soil ecological environment.

[0023] The communication load of intelligent communication equipment refers to the amount of data or information capacity that the equipment can process and transmit during the communication process. It is one of the important indicators for measuring the data processing capability of intelligent communication equipment.

[0024] The monitoring data sampling time of the intelligent communication device refers to the time interval for the intelligent communication device to receive monitoring data from the environment during data monitoring. It can also be the time point or time period when the device receives the data. The time interval is determined based on the actual situation of the ecological environment, which is conducive to the accuracy and real-time nature of the monitoring data.

[0025] The above-mentioned fractional-order adaptive spread spectrum combines the characteristics of fractional-order Fourier transform and adaptive spread spectrum technology. The fractional-order Fourier transform allows the signal to be rotated or transformed a non-integer number of times between the time domain and the frequency domain. The above-mentioned transformation provides flexibility and freedom for data signal processing, and is particularly suitable for processing non-stationary information and data signals with time-varying characteristics. Based on this, the signal spectrum corresponding to the database is expanded to occupy a very wide frequency band, which can improve the anti-interference ability and transmission efficiency of intelligent communication equipment, and thus adapt to different communication conditions and black soil ecological environment detection needs.

[0026] The number of monitoring data collection points of the intelligent communication device refers to the total number of points set by the intelligent communication device for collecting monitoring data in a specific monitoring area of ​​the black soil ecological environment. The above-mentioned collection points can be physically arranged sensor nodes or virtual data collection points, which together constitute a system monitoring network, which helps to comprehensively and accurately obtain the ecological environment data in the monitoring area. In the embodiment, there are a total of N monitoring data collection points.

[0027] The data subsets of different monitoring data collection points refer to the data sets collected and stored by each independent monitoring data collection point in the distributed monitoring system. The above data subsets together constitute the data set of the entire monitoring system. Each subset contains specific ecological environment parameters or related data monitored by different collection points. In the embodiment, the data subsets of different monitoring data collection points respectively satisfy the following relationship: .

[0028] In a communication system, the communication bandwidth of an intelligent communication device refers to the frequency range occupied by a data signal, that is, the difference between the highest frequency and the lowest frequency of the data signal.

[0029] In an optional embodiment, a communication abnormal data detection index is set. The communication abnormal data detection index mainly includes a data leakage rate of the intelligent communication device. The data leakage rate needs to satisfy the following relationship: in, Indicates the data leakage rate of intelligent communication equipment, Represents a collection of data sent to an intelligent communication device. Represents a set of monitoring data received by the intelligent communication device.

[0030] The detection index of the embodiment is the data leakage rate of the intelligent communication device. In order to evaluate the performance of this index, two data sets are first set based on the dynamic monitoring data set and the original database. One is the data set sent to the intelligent communication device. , which represents all the data sent by the system or network to the intelligent communication device; the other is the monitoring data set actually received by the intelligent communication device , which represents the data successfully received and processed by the intelligent communication device. Based on the above two data sets, the data leakage rate of the intelligent communication device can be calculated.

[0031] Furthermore, a detection index threshold is set. If the calculated data leakage rate is greater than the detection index threshold, it is considered that there is a problem with the receiving performance of the intelligent communication device and communication data optimization is required. At the same time, the dynamic monitoring database can be updated or the communication equipment can be inspected and maintained.

[0032] This embodiment sets a communication data optimization model in the analysis and processing module. The model reduces the sum of square distances between data sample points and their cluster centers through iterative optimization, so that the dynamic monitoring data of the black soil ecological environment is more in line with the actual situation.

[0033] The above communication data optimization model satisfies the following relationship: in, Represents the reference value for data iterative optimization. Indicates the number of monitoring data collection points of intelligent communication devices, Represents the data sample points in different monitoring data collection points, Represents the data set of all monitoring data collection points, Represents the cluster center of data samples at different monitoring data collection points.

[0034] in It is a numerical indicator to measure the degree of data optimization, based on which the clustering effect and optimization degree of the data can be intuitively reflected. When the value is small, it means that the data sample point is closer to its cluster center and the data clustering effect is good; otherwise, it means that the data clustering effect is poor. If the data leakage rate of the intelligent communication device exceeds the detection index threshold, the data needs to be input into the communication data optimization model for iterative optimization until the reference value meets the minimum reference value or the optimization model reaches the maximum number of iterations.

[0035] The data sample points in each monitoring data collection point together constitute the data subsets of different monitoring points, and the above data sample points all belong to the data set , that is, satisfy .

[0036] All data sample points held by each monitoring data collection point are aggregated into a large set. Specifically, It is a data set composed of n different data subsets. Different subsets correspond to different monitoring data collection points and are recorded as ,therefore .

[0037] Each of the monitoring data collection points has a corresponding cluster center, and the above cluster centers together constitute a set, denoted as .in, represents the cluster center of the first monitoring point data sample, represents the second monitoring point, and so on, until Represents the cluster center of N monitoring points. The above cluster center is obtained by calculating the mean of the data samples of each monitoring point, so it can also be called the mean representative point or cluster center point of the data subset.

[0038] In the embodiment, As an optimization reference value, it is used to evaluate the overall operation process of the communication data optimization model. The above model adjusts the location of the cluster center of different monitoring data collection points through continuous iteration, with the aim of minimizing the error square sum function The value of is recalculated every time the model completes a round of iteration In the process of iterative model optimization The value will show a gradually decreasing trend because as the location of the cluster center is gradually optimized, the distance between the data sample point and its corresponding cluster center will gradually shorten, which will make the error square sum function value continue to decrease. However, when the algorithm iterates to a certain stage, The value will no longer change significantly. At this time, it can be considered that the data set has reached a relatively stable state. At this time, you can choose to terminate the iterative optimization and obtain a dynamic monitoring data set of the black soil ecological environment.

[0039] In an optional embodiment, the original monitoring data set of the ecological environment of the black soil is obtained based on the communication device receiving data analysis model in the analysis and processing module. That is, the data quality of the original monitoring data set is evaluated using the communication abnormal data detection index; and the original monitoring data set is iteratively optimized based on the data quality and the communication data optimization model, and a dynamic monitoring data set of the ecological environment of the black soil is obtained.

[0040] First, the data analysis model is received through the communication equipment, and the original monitoring data set about the ecological environment of the black soil is obtained in the analysis and processing module. The sources of the above data in the embodiment include but are not limited to domestic and foreign literature reviews, historical monitoring records and the current ecological status of the black soil, which helps to achieve a preliminary understanding of the ecological environment of the black soil.

[0041] Subsequently, in order to evaluate the reliability and accuracy of the above-mentioned original monitoring data set, the communication anomaly data detection index is used in the embodiment to quantitatively evaluate its data quality. The evaluation results can clearly understand the data quality of the original monitoring data set, which is helpful to identify problems and anomalies in the data and provide guidance for subsequent data processing and optimization.

[0042] After determining the data quality situation, we further adopted an iterative optimization strategy based on the data quality situation and the communication data optimization model. Through continuous iteration and adjustment, the original monitoring data set was optimized, thereby obtaining a more accurate and reliable dynamic monitoring data set of the black soil ecological environment. The above data set is conducive to the real-time monitoring and accurate control of the changing trends of the black soil ecological environment.

[0043] In this embodiment, a comparative analysis is conducted on the original monitoring data set and the dynamic monitoring data set of the black soil ecological environment. Figure 2 The signal changes between the original data and the final monitoring data in the intelligent communication device are intuitively displayed. Figure 2 It can be clearly seen that after the optimization processing of the analysis and processing module, the fluctuation trend of the data set has been significantly reduced, almost showing a linear and stable change, which further shows that the optimized data set avoids the fluctuation of data volume, thereby ensuring the stability and reliability of the data received by the system.

[0044] Furthermore, the construction method and steps of the analysis and processing module in the embodiment are merely an optional condition of the present invention. In one or some other embodiments, the analysis and processing module can be adjusted and optimized according to the actual situation of the black soil ecological environment and the dynamic monitoring needs, so that the analysis and processing module can better adapt to different regions, different time periods and different monitoring target needs, further ensuring the accuracy and reliability of the monitoring data and improving the implementation effect of the black soil ecological dynamic monitoring method.

[0045] S2. Set the monitoring demand parameters of the black soil ecological environment based on the dynamic monitoring data set, and build a multi-level assessment framework for the black soil ecological environment by combining the dynamic monitoring data set and the monitoring demand parameters. The specific steps and implementation contents are as follows: In the embodiment, the ecological pressure state response model is used to conduct in-depth analysis of the dynamic monitoring data set. The above model can accurately identify the current state of the black soil ecological environment and the pressure it is under, and set the monitoring demand parameters accordingly to ensure that the black soil ecological dynamic monitoring method can focus on key issues for monitoring and analysis.

[0046] The main problems facing the black soil ecological environment are understood through the ecological pressure state response model. The above problems include but are not limited to soil degradation, water imbalance, and biodiversity reduction. Based on the model identification results, the core monitoring needs are further refined. In order to comprehensively and accurately reflect the real situation of the black soil ecological environment and facilitate the actual implementation of the dynamic monitoring method, the pressure state information of the above black soil ecological environment can be found in Table 1.

[0047] Table 1 Information on the pressure status of the ecological environment of black soil Based on Table 1 and the dynamic monitoring data set, it can be seen that the ecological pressure state response model can clearly understand the main problems facing the black soil ecological environment. The above problems include but are not limited to soil degradation, water imbalance, biodiversity reduction, etc. Based on the model identification results, the core monitoring needs are further refined, which mainly include arable land quality monitoring, environmental quality monitoring, food safety monitoring, and sustainability monitoring, aiming to comprehensively and accurately reveal the true situation and actual appearance of the black soil ecological environment.

[0048] Based on this, we can better understand and implement the dynamic monitoring method. In the embodiment, the pressure status information of the black soil ecological environment is organized into Table 1. The above Table 1 progresses from the driving force layer, pressure layer, state layer, impact layer to the response layer, and lists in detail the various factors affecting the black soil ecological environment, providing a powerful reference indicator for protecting and monitoring the black soil ecological environment.

[0049] Furthermore, it is necessary to build a multi-level assessment framework for the black soil ecological environment by combining the analytic hierarchy process, monitoring demand parameters and dynamic monitoring data sets.

[0050] Furthermore, in order to comprehensively evaluate the ecological environment status of black soil, the hierarchical analysis method, monitoring demand parameters and dynamic monitoring data sets are combined to construct a multi-level and multi-dimensional black soil ecological environment assessment framework.

[0051] In the process of constructing the multi-level evaluation framework, it is necessary to preliminarily determine the indicator sets at each level. The above indicators will cover all levels from the driving force layer to the impact layer and then to the response layer, which is conducive to comprehensively capturing all aspects and influencing factors of the black soil ecological environment. By systematically organizing the indicators, a clear and orderly multi-level evaluation framework can be established. For specific information on the above multi-level evaluation framework, please refer to Table 2. Combining the multi-level evaluation framework and the dynamic monitoring data set can quantitatively analyze and comprehensively evaluate the actual situation of the black soil ecological environment, which not only helps to identify key environmental problems, but also guides the formulation of protection plans and improvement measures.

[0052] Table 2 Multi-level evaluation framework information table To sum up, a comprehensive and systematic multi-level assessment framework for the black soil ecological environment is constructed by combining the analytic hierarchy process, monitoring demand parameters and dynamic monitoring data sets, which provides strong support for the scientific and objective assessment of the ecological environment status of the black soil.

[0053] Furthermore, the construction method of the multi-level evaluation framework in this embodiment is only an optional condition of this embodiment. In one or some other embodiments, the construction method of the multi-level evaluation framework can be adjusted according to the evaluation requirements of the ecological environment and the ecological dynamic monitoring information. This allows the multi-level evaluation framework to better adapt to different regions, different ecosystems and different evaluation target requirements, further ensure the accuracy and pertinence of the ecological environment monitoring results, and improve the actual application effect of the ecological dynamic monitoring method.

[0054] S3. Introduce fuzzy comprehensive evaluation method to screen and weight the multi-level evaluation framework, and obtain the screening results and weight distribution results of the multi-level evaluation framework. The specific steps and implementation contents are as follows: In the embodiment, the first-level relative weight of the first-level indicator layer in the multi-level evaluation framework to the black soil ecology is analyzed based on the judgment matrix; the second-level relative weight of the second-level indicator layer to the first-level indicator layer in the multi-level evaluation framework is analyzed based on the judgment matrix; At the same time, the first evaluation threshold and the fuzzy comprehensive evaluation method are used for screening, and a secondary indicator comprehensive scoring set under the primary indicator layer is constructed; the second evaluation threshold and the fuzzy comprehensive evaluation method are used for screening, and a tertiary indicator comprehensive scoring set under the secondary indicator layer is constructed.

[0055] The fuzzy comprehensive evaluation method is used to screen the indicator sets at each level in the multi-level evaluation framework, and reasonable weights are assigned to different levels. For the weight allocation of the first-level indicators, the nine-scale judgment matrix is ​​used to determine the relative importance of the first-level indicator layer to the target layer.

[0056] First, construct a judgment matrix, where the elements It indicates the importance of index i relative to index j, that is, the relative importance between indicators is inverse to each other. The above judgment matrix satisfies the following relationship: .

[0057] Next, in order to evaluate the consistency of the judgment matrix, a consistency index CI is defined, which is equal to the eigenvalue of the judgment matrix Subtract the rank of a matrix , and then divided by , the above indicators reflect that the judgment matrix is ​​consistent to a certain extent, and the above consistency indicators satisfy the following relationship: .

[0058] In order to evaluate consistency more accurately, a random consistency index RI is introduced. It is obtained by randomly generating judgment matrices multiple times and calculating CI values, and then taking the average value. The RI value is related to the order of the judgment matrix. The above random consistency index satisfies the following relationship: .

[0059] The consistency test can be performed based on the consistency index CI and the random consistency index RI. By calculating the consistency ratio CR, it can be determined whether the weight vector of the judgment matrix is ​​reliable. When CR is less than 0.1, it is considered that the judgment matrix meets the consistency requirements and its weight vector is reliable; otherwise, the judgment matrix needs to be reconstructed until it meets the consistency test.

[0060] The above consistency ratio satisfies the following relationship: .

[0061] If the judgment matrix passes the consistency test, the matrix eigenvalue method can be used to calculate the weights of indicators at different levels. The above method is achieved by solving the eigenvalues ​​and eigenvectors of the judgment matrix, and then selecting the eigenvector corresponding to the maximum eigenvalue as the weight vector. In summary, based on the fuzzy comprehensive evaluation method and the nine-scale judgment matrix, the indicator sets at all levels are scientifically screened and weighted, thus providing technical support for the comprehensive evaluation and monitoring methods of the black soil ecological environment.

[0062] The weight distribution results of cultivated land quality, environmental quality, food safety and sustainability in the first-level indicator layer of the embodiment are shown in Table 3.

[0063] Table 3 Weight distribution table of the first-level indicator layer The screening process of the secondary indicator layer of the embodiment is as follows; When using the fuzzy comprehensive evaluation method to construct a secondary indicator layer scoring system under the primary indicator layer, we first need to determine five key factors: relevance, authority, operability, sustainability and monitoring cost. The above factors together constitute the factor set F, which needs to satisfy the following relationship: F={relevance, authority, operability, sustainability, monitoring cost}.

[0064] Next, the weight vectors WF of the above factors in the evaluation are calculated through the judgment matrix, and they are 0.29, 0.11, 0.29, 0.15 and 0.16 respectively. That is, the factor weight vectors satisfy the following relationship: At the same time, the comment set is set to include the score vector E, which mainly includes five levels, with corresponding scores of 100, 80, 60, 40 and 20, namely .

[0065] In order to conduct fuzzy comprehensive evaluation, we first need to construct a membership matrix R, which is based on the evaluation table to construct, where It indicates the number of experts who give a certain evaluation level j for a certain factor i, and the membership degree The calculation formula satisfies the following relationship: , based on which the membership of each factor in each evaluation level can be obtained.

[0066] After obtaining the membership matrix R, the weight vector WF and the membership matrix R can be used to calculate the comprehensive evaluation vector S. The calculation formula of the evaluation vector satisfies the following relationship: , which means multiplying the weight vector by the membership matrix, and the final result is the comprehensive evaluation vector.

[0067] Next, the comprehensive evaluation vector S and the score vector E are used to calculate the comprehensive evaluation value Value. The calculation formula satisfies the following relationship: , which means that the dot product operation is performed on the comprehensive evaluation vector and the score vector, and the result is the comprehensive evaluation value.

[0068] Finally, it is necessary to set a comprehensive assessment threshold T. In the embodiment, the comprehensive assessment threshold is set to 60. By comparing the comprehensive assessment value of each secondary indicator layer with the comprehensive assessment threshold, indicators above the threshold can be screened out, thereby forming a final indicator set, which can be used in subsequent ecological environment assessment and decision-making processes.

[0069] For information on the indicator set finally formed by the first-level indicator layer and the second-level indicator layer of the embodiment, please refer to Table 4.

[0070] Table 4 Information table of primary and secondary indicator sets In the process of allocating weights of secondary indicators, the embodiment uses the nine-scale method to construct a judgment matrix. The above method can determine the importance of each secondary indicator layer relative to the primary indicator layer to which it belongs through expert scoring or collective discussion. The elements in the judgment matrix represent the relative importance ratios between different secondary indicator layers. The relevant ratios are given based on the nine-scale method, that is, integers from 1 to 9 and their reciprocals are used to represent the importance differences between indicators.

[0071] After the judgment matrix is ​​constructed, the weight vector WF of the secondary indicator can be obtained by solving the eigenvalue and eigenvector of the matrix. This weight vector reflects the proportion or importance of each secondary indicator layer under the primary indicator layer. In addition, the calculation process of the weight vector needs to meet certain mathematical conditions, such as the eigenvalue must be positive and the largest, and the corresponding eigenvector is the required weight vector.

[0072] In this embodiment, the relative weight values ​​of the secondary indicator layer are summarized in Table 5.

[0073] Table 5 Relative weight table of secondary indicator layer The screening results of the third-level indicator layer of the embodiment are as follows: First, please refer to Table 6 for the corresponding information of the third-level indicator layer before screening.

[0074] Table 6 Information table before the third-level indicator layer screening In the embodiment, the scoring set corresponding to the third-level indicator layer under the second-level indicator layer is also constructed based on the fuzzy comprehensive evaluation method. When constructing the scoring system of the third-level indicator set under the second-level indicator layer, the fuzzy comprehensive evaluation method is used to determine two key third-level indicator layer factors, mainly sensitivity and reliability, which together constitute the factor set F.

[0075] Next, a corresponding weight is assigned to each factor to form a factor weight vector WF. In this embodiment, the weights of sensitivity and reliability are both 0.5, indicating that the above factors have equal importance in the evaluation.

[0076] In order to quantitatively evaluate the three-level indicator layer, a comment set score vector E is also set. The above vector mainly contains four evaluation levels, and each level corresponds to a specific score. That is, the highest level score is 100, followed by 70, 40 and 10 respectively. The above scores directly reflect the differences between different evaluation levels.

[0077] After determining the factor set, factor weight vector and comment set / score vector, the scoring system of the third-level indicator layer is constructed by referring to the screening steps of the second-level indicator layer, which includes but is not limited to the steps of constructing a membership matrix, calculating a comprehensive evaluation vector and a comprehensive evaluation value, so as to obtain the comprehensive evaluation value corresponding to the third-level indicator layer.

[0078] Please see Table 7 for the three-level indicator layer information after screening.

[0079] Table 7 Information table after screening of the three-level indicator layer Furthermore, in the present embodiment, the screening method and weight distribution method of the multi-level evaluation framework are only an optional condition of the present embodiment. In one or some other embodiments, the analysis method of the multi-level evaluation framework can be adjusted according to the structural conditions of the multi-level evaluation framework and the actual conditions of the ecological monitoring information. The present invention customizes the analysis method and can more accurately capture the key information of the ecological environment, thereby improving the pertinence and accuracy of the black soil ecological monitoring results, helping to more comprehensively understand the actual conditions of the ecological environment and provide a scientific basis for formulating effective protection and management strategies.

[0080] S4. Establish a numerical transformation evaluation and feedback mechanism, obtain the score evaluation results and grade classification results of the multi-level evaluation framework based on the numerical transformation evaluation and feedback mechanism and the dynamic monitoring data set, and combine the score evaluation results, grade classification results and dynamic monitoring data set to achieve dynamic analysis and real-time monitoring of the black soil ecological environment. The specific steps and implementation contents are as follows: A numerical transformation evaluation and feedback mechanism is established based on a multi-level evaluation framework. In the embodiment, the numerical transformation evaluation and feedback mechanism mainly includes a multi-level indicator numerical normalization method; and based on the multi-level indicator numerical normalization method and a dynamic monitoring data set, score evaluation results and grade classification results of different indicator layers in the multi-level evaluation framework are obtained.

[0081] First, a numerical transformation evaluation and feedback mechanism is established. In order to quantitatively evaluate the fertility of cultivated land, soil biodiversity, and ecological environment quality, a numerical transformation evaluation and feedback mechanism is constructed in the embodiment. This mechanism comprehensively reflects the actual situation in various fields through multi-level indicators, and normalizes different indicator layers, which helps to enhance the comparability and comprehensive evaluation between different indicator layers.

[0082] The above-mentioned numerical transformation evaluation and feedback mechanism mainly includes the multi-level indicator numerical normalization method. In the numerical transformation evaluation process, the normalization method can eliminate the differences between different indicator layers due to different dimensions and value ranges, so that each indicator layer has the same weight and influence in the evaluation system.

[0083] The normalization processing method of the multi-level indicator values ​​in the embodiment needs to satisfy the following relationship: in, It represents the normalized result of index i, which is a dimensionless value, and its value range is usually between 0 and 1.

[0084] It represents the measured value of indicator i, which is the original data directly obtained from monitoring data or database.

[0085] The normalization coefficient of indicator i is a constant used to adjust the value range of the indicator. The selection of the above normalization coefficient should be determined based on the actual value range of the indicator, the evaluation objectives and the overall design of the evaluation system.

[0086] Determination of the normalization coefficient of each indicator For each indicator level under arable land fertility, soil biodiversity and ecological environment quality, it is necessary to determine the normalization coefficient based on the actual value range, importance and evaluation needs.

[0087] Please refer to Table 8 for the normalization coefficients of the indicators under the fertility of cultivated land, soil biodiversity, and ecological environment quality in the embodiment.

[0088] Table 8 Normalized information table of cultivated land fertility, soil biodiversity and ecological environment quality In an optional embodiment, the analysis steps and related contents of the normalization coefficient are as follows: Data collection and analysis: First, based on the dynamic monitoring data set, the measured data of each indicator is collected, and preliminary analysis and statistics are carried out to understand the value range, distribution characteristics and change patterns of different indicators.

[0089] Preliminary setting of normalization coefficient: Based on the data analysis results of different indicators, the normalization coefficient of each indicator is preliminarily set, which can be achieved through expert consultation, historical data comparison, statistical analysis and other methods.

[0090] Coefficient adjustment and optimization: After the normalization coefficients are initially set, multiple adjustments and optimizations are required, which can be achieved through repeated verification and correction of the evaluation results to further ensure the accuracy and reliability of the evaluation system.

[0091] Final determination: After adjustment and optimization, the normalization coefficients of each indicator are finally determined and used in subsequent black soil ecological assessment and monitoring tasks.

[0092] The normalized results of the indicators under soil cleanliness, groundwater quality, irrigation water quality, and agricultural product quality in the embodiment are shown in Table 9.

[0093] Table 9 Normalized information table for soil cleanliness, groundwater quality, irrigation water quality, and agricultural product quality The evaluation score calculation and evaluation level of the embodiment for the secondary indicator layer and the primary indicator layer are as follows: Evaluation score calculation for the secondary indicator layer.

[0094] In order to obtain the evaluation score result of the second-level indicator layer, it is necessary to find the sum of the scores of all the third-level indicators under the second-level indicator layer, and then divide it by the number of indicators n in the third-level indicator layer. The above evaluation score results need to satisfy the following relationship; in, represents the evaluation score of the secondary indicator layer j, Indicates the third-level indicators under different second-level indicators, Indicates the number of third-level indicators under different second-level indicators.

[0095] Evaluation calculation and grading standards for the first-level indicator layer.

[0096] The evaluation index of the first-level indicator k is obtained by combining all the second-level indicators under it The score and its corresponding weight coefficient That is, for each secondary indicator j, its score is Multiply by the weight factor , and then add all the products together to satisfy the following relationship: in, represents the sum operation, represents the evaluation index of the first-level indicator k, represents the evaluation score of the secondary indicator layer j, Represents the weight coefficient of the secondary index j.

[0097] After obtaining the evaluation index of the first-level indicator k After that, the evaluation level can be judged according to the preset grading standards. Different score ranges can be set to correspond to different evaluation levels, including but not limited to excellent, good, medium, pass and fail. The above grading standards need to be set according to the actual black soil ecological environment and monitoring needs, which is conducive to converting the evaluation results into evaluation levels that are easy to understand and apply.

[0098] Please refer to Table 10 for the classification and grading results of the first-level indicator layer in the embodiment.

[0099] Table 10 Classification and grading information table of the first-level indicator layer The calculation and classification standards for the comprehensive quality assessment of the ecological environment of black soil in the embodiment are as follows: The above-mentioned black soil ecological environment comprehensive quality assessment index needs to meet the following conditions: in, represents the comprehensive quality assessment index of the black soil ecological environment. represents the evaluation index of the first-level indicator k, Represents the weight coefficient of the first-level indicator k.

[0100] Furthermore, the comprehensive quality assessment index of the black soil ecological environment is shown in Table 11.

[0101] Table 11 Comprehensive evaluation information of black soil ecological environment The black soil ecological environment dynamic monitoring method based on intelligent communication in the embodiment has the following specific process: Firstly, the communication equipment receiving data analysis model, communication abnormality data detection index and communication data optimization model in the analysis and processing module were used to collect and organize the dynamic monitoring data set of the black soil ecological environment. Based on the above models and detection indicators, the dynamic monitoring data set of the black soil ecological environment was obtained.

[0102] Next, based on the dynamic monitoring data set, we understood and identified the main problems facing the black soil ecology, and adopted a pressure response model to construct a multi-level assessment framework. The above framework helps to clearly understand the pressures faced by the black soil ecological environment and its response mechanism.

[0103] On this basis, core monitoring requirements were proposed and the analytic hierarchy process was used to establish a hierarchical and classified indicator system framework, which not only covers a wide range of monitoring, but also ensures the comprehensiveness and systematicness of indicator parameters.

[0104] In order to further improve the monitoring indicator system, the embodiments refer to national standards, local standards and industry standards, and supplement and optimize the initially established indicator system based on the above dynamic monitoring data set, so as to further ensure that the monitoring indicator system is both compliant with the regulations and practical.

[0105] Subsequently, the fuzzy comprehensive evaluation method was used to screen and optimize the index system. The above method can more scientifically and objectively determine the importance and impact of different indicators in evaluating the ecological environment of black soil.

[0106] After determining the indicator system, the judgment matrix was used to assign weights to the indicator layers, thereby ensuring that the weight of each indicator in the evaluation system matches its importance. Next, the normalization method was used to convert the measured values ​​of the three-level indicators into scores, which can more intuitively understand the value of each indicator and lay the foundation for subsequent calculations. On this basis, the evaluation scores of the previous level were calculated in turn, thereby providing a comprehensive evaluation result on the overall status of the black soil ecological environment.

[0107] Finally, in order to classify the target layer and the criterion layer, a level judgment threshold is also set up, which is conducive to a clearer division of the levels of different evaluation results, and provides support for the formulation of targeted environmental protection measures and management strategies. In summary, the present invention obtains a dynamic monitoring data set of the black soil ecological environment through a series of scientific and systematic steps, and at the same time constructs a monitoring and evaluation system for the black soil ecological environment, which provides an information basis and technical guarantee for the protection and sustainable development of the black soil ecological environment.

[0108] See also Figure 3In an optional embodiment, in order to efficiently execute the black soil ecological environment dynamic monitoring method based on intelligent communication provided by the present invention, the present invention also provides a black soil ecological environment dynamic monitoring system based on intelligent communication, the above-mentioned black soil ecological environment dynamic monitoring system based on intelligent communication includes a processor, an input device, an output device and a memory, the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the specific steps of the relevant embodiments of the black soil ecological environment dynamic monitoring method based on intelligent communication provided by the present invention. The black soil ecological environment dynamic monitoring system based on intelligent communication of the present invention has a complete structure, objective and stable, and can efficiently execute the black soil ecological environment dynamic monitoring method based on intelligent communication of the present invention, thereby improving the overall applicability and practical application ability of the present invention.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for dynamic monitoring of the ecological environment of black soil based on intelligent communication, characterized in that: The steps include: An analysis and processing module for dynamic monitoring data of black soil ecology is set in the intelligent communication device, and a dynamic monitoring data set of the ecological environment of black soil is obtained by using the analysis and processing module; According to the dynamic monitoring data set, monitoring requirement parameters of the black soil ecological environment are set, and a multi-level evaluation framework of the black soil ecological environment is constructed by combining the dynamic monitoring data set and the monitoring requirement parameters; Introducing a fuzzy comprehensive evaluation method to screen and weight the multi-level evaluation framework, and obtaining a screening result and a weight distribution result of the multi-level evaluation framework; A numerical transformation evaluation and feedback mechanism is established, and score evaluation results and grade classification results of a multi-level evaluation framework are obtained based on the numerical transformation evaluation and feedback mechanism and the dynamic monitoring data set. The dynamic analysis and real-time monitoring of the black soil ecological environment are realized by combining the score evaluation results, the grade classification results and the dynamic monitoring data set.

2. The method for dynamic monitoring of black soil ecological environment based on intelligent communication according to claim 1 is characterized in that: The analysis and processing module for the black soil ecological dynamic monitoring data is set in the intelligent communication device, including: An analysis and processing module for the dynamic monitoring data of the black soil ecology is set up based on the monitoring conditions of the intelligent communication equipment. The analysis and processing module includes a communication equipment receiving data analysis model, a communication abnormality data detection index, and a communication data optimization model.

3. The method for dynamic monitoring of black soil ecological environment based on intelligent communication according to claim 2 is characterized in that: The communication device receives a data analysis model that satisfies the following relationship: in, Represents the monitoring data set received by the intelligent communication device, Indicates the communication load of intelligent communication devices, Indicates the monitoring data sampling time of the intelligent communication device, Represents the fractional adaptive spread spectrum of intelligent communication equipment, Indicates the number of monitoring data collection points of intelligent communication devices, represents the data subsets at different monitoring data collection points, Indicates the communication width of the intelligent communication device.

4. The method for dynamic monitoring of black soil ecological environment based on intelligent communication according to claim 2 is characterized in that: The analysis and processing module for the dynamic monitoring data of the black soil ecology is set based on the monitoring situation of the intelligent communication device, and the analysis and processing module includes a communication device receiving data analysis model, a communication abnormal data detection index, and a communication data optimization model, including: Setting a communication abnormal data detection indicator, wherein the communication abnormal data detection indicator includes a data leakage detection rate of the intelligent communication device; The data missed detection rate satisfies the following relationship: in, Indicates the data leakage rate of intelligent communication equipment, Represents a collection of data sent to an intelligent communication device. Represents a set of monitoring data received by the intelligent communication device.

5. The method for dynamic monitoring of black soil ecological environment based on intelligent communication according to claim 2 is characterized in that: The communication data optimization model satisfies the following relationship: in, Represents the reference value for data iterative optimization. Indicates the number of monitoring data collection points of intelligent communication devices, Represents the data sample points in different monitoring data collection points, Represents the data set of all monitoring data collection points, Represents the cluster center of data samples at different monitoring data collection points.

6. The method for dynamic monitoring of black soil ecological environment based on intelligent communication according to claim 2 is characterized in that: The dynamic monitoring data set of the black soil ecological environment obtained by using the analysis and processing module includes: Based on the communication equipment receiving data analysis model in the analysis and processing module, the original monitoring data set of the ecological environment of the black soil is obtained; Using the communication anomaly data detection index to evaluate the data quality of the original monitoring data set; The original monitoring data set is iteratively optimized based on the data quality situation and the communication data optimization model, and a dynamic monitoring data set of the black soil ecological environment is obtained.

7. The method for dynamic monitoring of black soil ecological environment based on intelligent communication according to claim 1 is characterized in that: The step of setting the monitoring requirement parameters of the black soil ecological environment according to the dynamic monitoring data set and constructing a multi-level evaluation framework of the black soil ecological environment in combination with the dynamic monitoring data set and the monitoring requirement parameters includes: Introducing an ecological pressure state response model, using the ecological pressure state response model to identify the dynamic monitoring data set, and setting monitoring demand parameters of the black soil ecological environment based on the identification results; A multi-level evaluation framework for the black soil ecological environment is constructed by combining the analytic hierarchy process, the monitoring demand parameters and the dynamic monitoring data set.

8. The method for dynamic monitoring of black soil ecological environment based on intelligent communication according to claim 1 is characterized in that: The introduction of the fuzzy comprehensive evaluation method to screen and weight the multi-level evaluation framework includes: The first-level relative weight of the first-level indicator layer in the multi-level evaluation framework for black soil ecology was analyzed based on the judgment matrix; Analyze the relative weight of the secondary indicator layer to the primary indicator layer in the multi-level evaluation framework based on the judgment matrix; Using the first evaluation threshold and the fuzzy comprehensive evaluation method to screen and construct a secondary indicator comprehensive scoring set under the primary indicator layer; The second evaluation threshold and the fuzzy comprehensive evaluation method are used for screening, and a three-level indicator comprehensive scoring set under the secondary indicator layer is constructed.

9. The method for dynamic monitoring of black soil ecological environment based on intelligent communication according to claim 1 is characterized in that: The establishment of the numerical transformation evaluation and feedback mechanism, and obtaining the score evaluation results and grade classification results of the multi-level evaluation framework according to the numerical transformation evaluation and feedback mechanism and the dynamic monitoring data set include: Establishing a numerical transformation evaluation and feedback mechanism based on a multi-level evaluation framework, wherein the numerical transformation evaluation and feedback mechanism includes a multi-level indicator numerical normalization method; According to the multi-level indicator value normalization method and the dynamic monitoring data set, score evaluation results and grade classification results of different indicator layers in the multi-level evaluation framework are obtained.

10. The black soil ecological environment dynamic monitoring system based on intelligent communication is characterized by: The system includes a processor, an input device, an output device and a memory, wherein the processor, input device, output device and memory are interconnected, wherein the memory is used to store a computer program, and the computer program includes program instructions. The processor is configured to call the program instructions to execute the black soil ecological environment dynamic monitoring method based on intelligent communication as described in any one of claims 1 to 9.

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